Vehicular traffic congestion leads to significant cost in terms of time, money and influence on the environment. To alleviate the effect through situational awareness, various traffic service providers, such as Navteq®, Inrix® and Total Traffic®, provide traffic and route information to the drivers. These information providers rely on a host of sensors, GPS probes, tollbooth data, Bluetooth sensors and so on, to collect information. The collected information is processed through proprietary methods and presented to the subscribers.
FIG. 1 illustrates an architectural diagram of a traditional infrastructure-based traffic information system 100 for implementing route calculation taking into account congestion information. The system 100 includes a data acquisition layer 102, for collecting traffic data from road sensors, cameras, probes and the like. The collected data can be related to accidents, roadwork and so on. The collected data are aggregated and processed in a traffic aggregation layer 104 including a central unit, which can be provided by service providers, such as Navteq®, Inrix® and so on. The central unit performs various functions, including the function of calculating reduced travel time routes for the vehicles on the roadways.
The data processed by the traffic aggregation layer 104 is subsequently distributed through a wireless distribution layer 106, which for example is implemented by FM or Satellite Radio. The information related to traffic congestion is fed to device layer 108 including in-vehicle navigation devices, smart phones or mobile phones, for conveying traffic information to drivers.
However, for the existing traffic navigation systems, the traffic information is limited to main roads. Thus, information related to the spillage onto arterial and side roads is barely available. This limits the ability to suggest alternate routes under most circumstances. Even on the major roads, the time to collect the information and send it to the users is significant. Various attempts are used to fit statistical distributions to the collected data. However, the accuracy, especially within short time frames (for example, a few minutes), suffers.
Lack of information about the state of the sensors also poses significant challenges for the traffic information aggregation. This is a result of lack of information about the status of GPS probes, their densities and other local conditions such as accidents, poor weather, road conditions, parking, short term congestion and so on. This significantly limits the ability of traffic information services to be responsive to the dynamic changes in the roadway environment.
Moreover, due to the centralized collection of all traffic-related data, it is extremely difficult to gather data from all the arterial and local roads for purposes such as route computation, which results in route computation based only on the starting conditions and very limited adaptation to altering traffic loads on different roads or road segments.
Accordingly, it is desirable to provide a distributed vehicle traffic navigation system and method which leverage a multi-hop vehicular network to gather local information and locally determine the shortest time travel paths independent of a central unit.
Further, it is desirable to provide a distributed vehicle traffic data management system and method which rely on distributed information aggregation of probe data and/or sensor data to build roadway traffic awareness and complement the services from traffic information providers.